abstract = "Dynamic job shop scheduling is a complex problem in
production systems. Automated design of dispatching
rules for these systems, particularly using the genetic
programming based hyper-heuristics (GPHH) has been a
promising approach in recent years. However, GPHH is a
computationally intensive and time consuming approach.
Parallel evolutionary algorithms are one of the key
approaches to tackle this drawback. Furthermore when
scheduling is performed under uncertain manufacturing
environments while considering multiple conflicting
objectives, evolving good rules requires large and
diverse training instances. Under limited time and
computational budget training on all instances is not
possible. Therefore, we need an efficient way to decide
which training samples are more suitable for training.
We propose a method to sample those problem instances
which have the potential to promote the evolution of
good rules. In particular, a sampling heuristic which
successively rejects clusters of problem instances in
favour of those problem instances which show potential
in improving the Pareto front for a dynamic
multi-objective scheduling problem is developed. We
exploit the efficient island model-based approaches to
simultaneously consider multiple training instances for
GPHH.",